Journal of Industrial Ecology最新文献

Sluggish progress on circular economy (CE) implementation is raising some concern that this allegedly new paradigm for sustainability is failing. While initial optimism had anticipated faster progress, actors across the private sector, government, consumer markets, and academia are embracing CE with varying speed and commitment. Circular principles are included in policy documents and corporate reports, and the circular start-up and scale-up realms are growing. Consumer awareness also appears to be rising, and a vibrant academic literature has emerged. We examine the roles of stakeholders in CE transition and pathways to acceleration and argue that the incrementalism often cited as a failure of CE is a fundamental characteristic of the paradigm; this notion forecloses the possibility of transformational change but supports an optimistic narrative. The characteristic of incrementalism makes CE progress metrics easier to measure and communicate, and we argue that, from this perspective, CE is making more progress than many critics suggest. This article elaborates on these points and argues for a more critical and provocative discourse around CE.

The transition to a sustainable economy requires investments in companies capable of driving real-world transformations. Impact assessments are central to this, yet existing company impact assessment tools for impact investing lack the necessary methods and data to determine the significance of environmental and social impacts. This paper addresses this gap by first exploring the life cycle assessment (LCA) literature on LCA logics and their application in company impact assessment tools. Second, we examine the conceptual and practical availability of absolute sustainability indicators for investment purposes. Our findings show that while LCA logics provide a valuable foundation for assessing the significance of company impacts, important gaps remain in allocating macro-level thresholds to the company level. Moreover, while environmental absolute sustainability indicators are conceptually advanced, their practical application is hindered by data limitations, restricting their usability for investors. Social absolute sustainability indicators lack clear impact pathways for translating macro-level issues into actionable company-level indicators, which is further constrained by data gaps. In light of these findings, we emphasize the distinct requirements of the environmental and social dimensions in advancing the assessment of the significance of company impacts. To effectively address these needs and enhance impact investment practices, we highlight the importance of interdisciplinary research, the regulatory and practical adoption of absolute sustainability approaches, and improved data integration.

In urban environments, region-specific knowledge of building material intensities and stocks is vital for efficient resource recovery in the construction sector, especially for city regeneration and redevelopment. Previous studies often relied on generalized data, leading to inaccuracies due to local variations in construction practices, materials, and urban density. This study addresses these limitations by developing a locally refined inventory through a GIS-based, bottom-up material stock analysis that integrates archetype-specific building design data, demonstrated with evidence from Hong Kong's public rental housing (PRH). The results show that material intensities for Hong Kong PRH buildings range from 1567 to 2386 kg/m², with a total stock of 60.85 megatons as of 2022. Up to 46.95 megatons may have recycling potential over the next three decades, offering significant opportunities for sustainable resource management. Spatiotemporal and hotspot identification reveals a shift in material stock distribution toward the northern territories, reflecting urban development trends. This research enhances the accuracy of material stock assessments and supports strategic planning for achieving a circular economy, particularly in densely populated areas like Hong Kong. By promoting circular and generative city concepts and establishing benchmark archives for key construction materials, the study advances practical applications for sustainable urban resource management, aiding policy development for efficient spatial planning and urban mining strategies.

Building material stock studies are essential for advancing the circular economy in construction. However, existing models often lack both accuracy and scalability. While machine learning has demonstrated significant potential to enhance predictive accuracy, its adoption has been hindered by a shortage of high-quality training data. In this study, we introduce a novel methodology leveraging a large language model to extract previously untapped building material data from building energy performance certificates with a focus on exterior walls. This approach enabled us to create a dataset of over 20,000 buildings—significantly larger than those used in previous studies. Leveraging this dataset, we developed a machine learning model to predict material composition based on building characteristics such as construction year, use, and location. Furthermore, we integrated knowledge of construction history to estimate the material stock of walls in terms of volume, mass, and associated CO₂ emissions for each building in the dataset. Our analysis revealed significant regional variations in material use patterns, emphasizing the critical role of location—a parameter often overlooked in existing building material stock models. These findings provide valuable insights for improving building stock modeling and highlight the importance of regionally tailored policies in advancing the circular economy in the construction sector.

Digital technologies have been transforming societies for decades (Lange et al., 2023), with published studies on their environmental impacts dating back to the early 2000s (e.g., Berkhout & Hertin, 2001; Fichter, 2002; Koomey, 2000). The public launch of ChatGPT in late 2022 and the subsequent rise of generative AI has sparked widespread interest in the environmental impacts of AI and wider information and communications technology (ICT) sector (Luers et al., 2024). For example, half of all assessments of data center energy use over the past decade have been published since January 2024 (Kamiya & Coroamă, 2025).

Digital technologies are used across economies and societies and result in wide-ranging environmental impacts. These impacts are typically classified into three types or “orders”: first order (direct), second order (indirect), and higher order (structural and behavioral) (Berkhout & Hertin, 2004; Hilty & Aebischer, 2015). Direct impacts describe the (always detrimental) environmental impacts from raw material extraction, production, use, and waste management of ICT devices and equipment. Indirect effects—which can be both detrimental or beneficial—arise from the use and implementation of digital technologies. Higher-order indirect effects can also be both positive and negative, and include changes to production and consumption patterns, various types of rebound effects, as well as learning and induction effects (Börjesson Rivera et al., 2014). Assessing each type of effect requires different data and methodologies.

Digital technologies and their applications are evolving quickly, making it increasingly challenging to conduct robust environmental assessments on their widespread uses and impacts. A major challenge is the lack of data as well as a lack of established methodologies, particularly when assessing indirect effects (Bergmark et al., 2020; Bremer et al., 2023; Coroama et al., 2020; Masanet et al., 2024). Digital technologies are deeply integrated and applied across many sectors, raising challenges in defining system boundaries and allocation of environmental impacts to specific technologies or services.

This special issue invited papers addressing either direct or indirect environmental impacts of specific or aggregated digital technologies as well as the environmental impact of digitalization at the application, network, sectoral, or macro level. The call for papers specifically welcomed studies addressing methodological challenges and advances in the assessment of environmental impact of the digital economy.

Table 1 provides an overview of the nine articles included in this special issue, which include methodological papers, case studies, a bibliometric analysis, and a book review. The articles consider both direct and i

Circular ecosystems have become a buzz concept in the circular economy, but differing meanings and theoretical foundations limit knowledge consolidation. To address this gap, we undertook a systematic literature review of the circular ecosystems literature published from 2004 to 2025. We analyzed the state-of-the-art using content analysis and science mapping techniques based on articles from Scopus, Web of Science, and Google Scholar databases. Prominent research trends include: (i) conceptualization of circular ecosystems, (ii) circular ecosystems and sustainability, (iii) roles and agency of actors, (iv) alignment in circular ecosystems, (v) value co-creation in ecosystems, (vi) governance of ecosystems, (vii) theoretical roots, and (viii) enablers of circular ecosystems. Furthermore, we present the etymology and evolution of circular ecosystems and present a comprehensive definition of the concept. Finally, we propose future research directions on circular ecosystems.

Advancements in life cycle assessment (LCA) and environmentally extended input–output analysis enable quick generic estimations of the environmental footprint of almost any product and service. However, going beyond a generic estimate to an assessment based on actual, specific supply chain data remains costly and impracticable and demands significant sharing of proprietary data between supply chain actors and the LCA practitioner. Achieving widespread specificity in LCA requires fundamentally changing the way inventory and emission data are collected, stored, and exchanged. This research develops the SSELF (Specific SEmiautomated Lifecycle Footprinting) framework to go beyond generic data in LCA in a way that can scale up, while safeguarding sensitive data. A key feature of the framework is decentralizing inventory collection and footprint calculations. Thus, production functions remain private and upstream impacts are calculated using an iterative approach with a database of unique product identifiers and the footprints reported by other users, capturing changes in the footprints of suppliers. Although this substantially reduces the effort of footprint assessments, implementing the framework in practice presents new challenges, which are identified and discussed in this paper along with recommendations on how they can be addressed and their implications. This work provides important insight into how to get to a point where every product and service has its unique footprint. Broad access to footprints with more specificity is necessary to help consumers reduce their consumption-based impacts and make companies take accountability for, and reduce, their indirect impacts.

Extending the lifespan of electronic devices through repair can significantly reduce negative environmental impacts. However, despite consumers reporting a strong preference for repair, actual repair rates remain relatively low. Qualitative research has indicated that uncertainty surrounding various aspects of repair service offers, such as repair costs and outcomes, discourages consumers from choosing repair. However, no studies have yet quantified the effects of varying degrees of uncertainty in repair service attributes on consumers' preferences for repair and repair services. This study addresses this gap using a dual-response choice-based conjoint analysis with 237 German consumers, manipulating the degree of uncertainty in the attributes repair success rate and total repair cost of hypothetical repair offers. Other attributes examined included repair service provider, repair time, and warranty. Results show that even vague information substantially increases preferences for repair compared to offering no information at all, nearly doubling the choice share of a repair offer. However, consumers may tolerate or even prefer some pricing uncertainty if it offers the possibility of a more favorable outcome. The study further reveals that consumers with prior repair experience tend to show higher willingness to choose repair services compared to those without such experience. Overall, the findings suggest that the lack of transparency about potential repair outcomes and costs, which is common in real-world repair offers, acts as a major barrier discouraging many consumers from choosing repair.

Environmental rating ecolabels (ERE) aim to present aggregated product environmental information to inform consumer choices. Performance ratings (e.g., A–E) are derived from life cycle assessment (LCA) results to enable comparison between products. The translation of LCA results to performance ratings requires definition of a common rating scale, either at a sector level or for subsets of products grouped by function (i.e., product categories). We investigate if and how assigning products to distinct categories influences final product ratings in ERE schemes. We consider if product categorization enables increased product differentiation and how the structure of categories affects the ratings awarded to products. Using a food sector case study, ratings were assigned to 2253 products based on aggregated environmental single scores derived from Agribalyse 3.1 and ratings obtained under three scenarios were compared: (1) no categorization—all products are placed on a single scale before rating; (2a) food group categorization—products are assigned to 11 food groups described in the Ciqual food composition database; and (2b) food subgroup categorization—products are assigned to 61 Ciqual food subgroups. We find that categorization has a significant influence on the final ratings, affecting at least 54% of products evaluated. Categorization restricts the range of products that can be compared but does not systematically improve differentiation within categories. For categorization to be used in ERE, categorization hierarchies need to be developed and harmonized at sector level reflecting consumer-relevant substitution options. This study demonstrates that categorization is a key methodological consideration for ERE scheme developers.